Image super-resolution rebuilding method based on confidence coefficient kernel regression

A technology of super-resolution reconstruction and confidence, which is applied in the field of image processing, can solve the problems of unsatisfactory suppression and unsatisfactory results, and achieve the effect of increasing the reliability of pixels, suppressing outliers, and strong robustness

Inactive Publication Date: 2014-08-06
HOHAI UNIV
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, under abnormal conditions such as severe electromagnetic interference, registration error, fuzzy estimation error, occlusion, etc., the image contains some extremely bright or extremely dark pixels or a very small area composed of several pixels,

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image super-resolution rebuilding method based on confidence coefficient kernel regression
  • Image super-resolution rebuilding method based on confidence coefficient kernel regression
  • Image super-resolution rebuilding method based on confidence coefficient kernel regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] Below in conjunction with the accompanying drawings, a kind of image super-resolution reconstruction method based on confidence kernel regression proposed by the present invention is described in detail:

[0032] Such as figure 1 As shown, the specific implementation process of an image super-resolution reconstruction method based on confidence kernel regression is as follows:

[0033] ① Input N frames of low-resolution images.

[0034] The input low-resolution images are all images polluted by warping, blurring, downsampling and noise.

[0035] ②Using the first frame image as a reference frame, estimate the motion parameters of the remaining frames.

[0036] The motion estimation method used is the commonly used motion estimation based on the optical flow method.

[0037] ③According to the motion parameters, project N frames of images into a standard grid to obtain a non-uniform distribution image z(x i ); the cells of the standard grid are all square, and the reso...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an image super-resolution rebuilding method based on the confidence coefficient kernel regression. According to the method, a confidence coefficient kernel function is constructed, the confidence coefficient kernel regression is carried out on an image through the confidence coefficient kernel function, interpolation in the image super resolution rebuilding is finished, by the adoption of the confidence coefficient kernel regression, not only is a double weighted mechanism of the self-adaption kernel regression inherited, but also distinguishing of pixel reliability is added, the abnormal value can be better restrained, and the image super-resolution rebuilding is achieved. According to the method, the image super-resolution rebuilding is achieved, the robustness is high, and the method can be widely applied to the fields such as monitoring, remote sensing, military, medical science and video entertainment and has the important value.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically refers to an image super-resolution reconstruction method based on confidence kernel regression. Background technique [0002] With the advent of the information age, digital imaging technology and related applications have achieved rapid development. Many CCD (charge-coupled device) and CMOS image sensors are widely used to acquire digital images. Although these sensors can meet most imaging applications, their resolution level and cost are far from meeting people's consumption needs, especially the needs of high-end technology research and development. For example, in digital imaging applications such as monitoring, remote sensing, military, medical and video entertainment, high-quality images require not only sufficient pixel density but also rich detail information. Due to the influence of technological level, cost and other factors, it is often not realistic to re...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T5/00
Inventor 徐枫陈哲徐立中严锡君石爱业
Owner HOHAI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap